Miklós Sebök

The classification of the items of ever-increasing textual databases has become an important goal for a number of research groups active in the field of computational social science. Due to the increased amount of text data there is a growing number of use-cases where the initial effort of human classifiers was successfully augmented using supervised machine learning (SML). In this project, Miklós Sebök investigates such a hybrid workflow solution classifying various source materials relevant for social research.